Reference-free or non-intrusive speech quality models are generally more useful than intrusive models for applications where obtaining a reference is inconvenient, impractical, or non-existent. The VoiceMOS challenge 2022 [1]} used a dataset [2]} that aggregated new raters on previous Blizzard Challenges and Voice Conversion speech data. The challenges were concerned with text-to-speech (TTS) and voice conversion applications, which are historically inconvenient to obtain a reference for. Non-intrusive models are desirable for a growing number of applications. For example, while intrusive quality models are well-suited to traditional codecs such as Opus, recent neural codecs can be problematic for full-reference quality models. Generative neural frameworks such as the Lyra codec [3]} and TacoTron [4]}, which use a WaveNet-based architecture [5]} produce output that is not typically aligned with the original reference signal, with natural-sounding deviations in pitch and time.
